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The final session of on-court combine activity unfolded Friday as 2018 draft hopefuls hit the floor looking to impress executives and head coaches at Quest Multisport in Chicago.

And with anthrometric testing complete, all eyes were on a pair of scrimmages, another round of meetings with the media and the last bit of strength and agility testing. As a reminder, most of the top prospects departed Chicago following Thursday's action.

Below, we'll break down the day's most notable moments and performers as stocks continue to fluctuate with just over a month remaining until the NBA draft gets underway June 21 at Barclays Center.

Complete combine results available through Tory Burch Womens Liana Ballet Flat oya9e07
.

Testing Numbers of Note

Player : Kostas Antetokounmpo, F, Greece

Player

Lane Agility : 12.40 seconds

Agility

Shuttle Run : 3.47 seconds

Shuttle Run

Three-Quarter Sprint : 3.21 seconds

Three-Quarter Sprint

Standing Vertical : 29.5 inches

Standing Vertical

Max Vertical : 35 inches

Max Vertical

Player : Jevon Carter, G/F, West Virginia

Lane Agility : 11.04 seconds

Lane Agility

Shuttle Run : 3.04 seconds

Three-Quarter Sprint : 3.18 seconds

Standing Vertical : 29 inches

Max Vertical : 36.5 inches

Player : Lonnie Walker IV, G, Miami

Lane Agility : 10.87 seconds

Three-Quarter Sprint : 3.06 seconds

Standing Vertical : 31.5 inches

Max Vertical : 40 inches

Player : Moritz Wagner, F/C, Michigan

Lane Agility : 11.48 seconds

Standing Vertical : 27 inches

Max Vertical : 34 inches

Scrimmage Standouts

If you're looking for a first-round sleeper, look no further than Kevin Hervey.

The former Texas-Arlington forward was the standout performer of the day's first scrimmage , inwhich he dropped a team-high 21 points on 5-of-7 shooting, including 4-of-5 from three. Hervey also got to the free-throw line nine times and made seven of his attempts.

Bleacher Report's Jonathan Wasserman, Sports Illustrated 's Jeremy Woo and ESPN.com's Mike Schmitz all came away impressed by the 2016-17 Sun Belt Player of the Year:

Kevin Hervey another sleeper having a nice few days. 6'8" with 7'3.5" length, effortlessly stroking threes. Athleticism a ? but NBA tools, convincing shot-making skills.

This presentation will cover a handful of the NLP building blocks provided by NLTK (and a few additional libraries), including extracting text from HTML, stemming lemmatization, frequency analysis, and named entity recognition. Several of these components will then be assembled to build a very basic document summarization program.

Obviously, you'll need Python installed on your system to run the code examples used in this presentation. We enthusiatically recommend using Anaconda , a Python distribution provided by Pantofola d`Oro FREDERICO UOMO MID Hightop trainers tortoise shell EDLKulDO
. Anaconda is free to use, it includes nearly 200 of the most commonly used Python packages for data analysis (including NLTK), and it works on Mac, Linux, and yes, even Windows.

We'll make use of the following Python packages in the example code:

Please note that the readability package is not distributed with Anaconda, so you'll need to download install it separately using something like or .

readability

If you don't use Anaconda, you'll also need to download install the other packages separately using similar methods. Refer to the homepage of each package for instructions.

You'll want to run one time to get all of the NLTK packages, corpora, etc. (see below). Select the "all" option. Depending on your network speed, this could take a while, but you'll only need to do it once.

One of the examples will use NLTK's interface to the Stanford Named Entity Recognizer , which is distributed as a Java library. In particular, you'll want the following files handy in order to run this particular example:

The first thing we'll need to do is :

The first time you run anything using NLTK, you'll want to go ahead and download the additional resources that aren't distributed directly with the NLTK package. Upon running the command below, the the NLTK Downloader window will pop-up. In the Collections tab, select "all" and click on Download. As mentioned earlier, this may take several minutes depending on your network connection speed, but you'll only ever need to run it a single time.

Now the fun begins. We'll start with a pretty basic and commonly-faced task: extracting text content from an HTML page. Python's urllib package gives us the tools we need to fetch a web page from a given URL, but we see that the output is full of HTML markup that we don't want to deal with.

(N.B.: Throughout the examples in this presentation, we'll use Python (e.g., below) to only display a small portion of a string or list. Otherwise, if we displayed the entire item, sometimes it would take up the entire screen.)

Fortunately, NTLK provides a method called to get the raw text out of an HTML-formatted string. It's still not perfect, though, since the output will contain page navigation and all kinds of other junk that we don't want, especially if our goal is to focus on the body content from a news article, for example.

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